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Protein remote homology detection and fold recognition are central problems in bioinformatics. In this paper, two kinds of profile-level building blocks of protein sequences, binary profiles and N-nary profiles, are presented, which contain the evolutionary information of the protein sequence frequency profile. The two building blocks are applied for protein remote homology and fold detection tasks...
Over the last years significant effort has been made to improve the performance of speech recognition. The Fisher Kernel has been suggested as good ways to combine and underlying generative model in the feature space and discriminant classifiers such as SVMs. Chinese name speech patterns are difficult to be classified especially when they are similar in pronunciation. Continuous density hidden Markov...
Protein remote homology detection is a central problem in bioinformatics. In this study, we present a novel building block of proteins called order profiles (OP) to use the evolutionary information of the protein sequence frequency profiles and apply this novel building block to remote homology detection. Order profiles contain the evolutionary information extracted from the protein sequence frequency...
One key element in understanding the molecular machinery of the cell is to understand the structure and function of each protein encoded in the genome. A very successful means of inferring the structure or function of a previously un-annotated protein is via sequence homology with one or more protein whose structure or function is already known. In this paper, a novel method for protein remote homology...
In this paper a new method that uses latent semantic analysis (LSA) to denote a protein sequence is proposed for researching the protein classification problem. A protein is vectorized according to its content of biological words: patterns and motifs, which are generated by utilizing TEIRESIAS algorithm and MEME/MAST system respectively. More precise description vectors of proteins are obtained through...
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